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      <title>MQ的消息如何去重 - 交互式测验</title>
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          <div class="quiz-container">
            <div class="quiz-header">
              <h1>MQ的消息如何去重 - 交互式测验</h1>
              <p>检验你对文档内容的理解程度</p>
            </div>
            <div id="quiz-content">
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  {
    "question": "根据文档，以下哪项不是导致消息重复的常见原因？",
    "options": [
      "网络不稳定导致生产者重试发送",
      "消息中间件故障恢复时重新投递消息",
      "消息中间件的负载均衡策略",
      "高并发环境下多个消费者处理了同一条消息"
    ],
    "correctAnswerIndex": 2,
    "explanation": "文档在「消息重复的原因」部分明确列举了几个原因，包括：网络问题、消息中间件的故障恢复、消息生产者的重复发送、以及并发环境下的操作。文档中并未提及负载均衡策略是导致消息重复的直接原因。",
    "source": "文档中「1. 消息重复的原因」部分",
    "hint": "请回顾文档中关于消息为何会重复的四个主要场景描述。"
  },
  {
    "question": "在消息去重策略中，“幂等性设计”的核心思想是什么？",
    "options": [
      "使用概率型数据结构快速判断消息是否存在，允许一定的误报率。",
      "确保同一操作无论执行多少次，其最终效果都与执行一次相同。",
      "在消息发送前，为每条消息生成一个全局唯一的ID。",
      "依赖消息中间件的自动确认机制来避免重复。"
    ],
    "correctAnswerIndex": 1,
    "explanation": "幂等性的定义是“一个操作可以执行多次，但无论执行多少次，其效果都与执行一次的效果相同”。这是幂等性设计的核心，旨在通过业务逻辑层面的设计来天然地防止重复操作带来的负面影响。",
    "source": "文档中「幂等性设计去重」的「1. 幂等性的定义」部分",
    "hint": "“幂等性”这个词本身就蕴含了“多次操作结果相同”的意义。"
  },
  {
    "question": "关于使用布隆过滤器进行消息去重，以下描述哪项是正确的？",
    "options": [
      "布隆过滤器能够100%准确地判断一个消息ID是否存在，绝不会出错。",
      "布隆过滤器支持高效地从集合中删除已处理的消息ID。",
      "布隆过滤器查询效率高且占用空间小，但存在误报的可能（false positive）。",
      "布隆过滤器通过对消息内容进行加密来保证唯一性。"
    ],
    "correctAnswerIndex": 2,
    "explanation": "文档明确指出，布隆过滤器是一种概率型数据结构。它的优点是空间效率高和查询速度快，但其核心挑战之一就是存在“误报率”，即它可能会判断一个元素存在于集合中，而实际上该元素并不存在。同时，标准的布隆过滤器不支持删除操作。",
    "source": "文档中「布隆过滤器去重」的「3. 布隆过滤器在消息去重中的优势和挑战」部分",
    "hint": "文档提到布隆过滤器是一种“概率型数据结构”，并指出了其一个关键的挑战。"
  },
  {
    "question": "文档指出了使用“数据库唯一索引”策略进行消息去重的一个主要缺点是什么？",
    "options": [
      "实现逻辑非常复杂，需要编写大量的业务代码。",
      "可能会影响数据库的插入性能，并在高并发时带来额外开销。",
      "无法保证数据的最终一致性。",
      "需要占用大量的内存空间来存储索引。"
    ],
    "correctAnswerIndex": 1,
    "explanation": "根据文档中“数据库唯一索引”的优缺点分析，其主要缺点是“可能会影响插入性能，对于高并发场景下的冲突处理可能带来额外开销”。因为每次插入操作，数据库都需要检查唯一性约束，这在高并发下会成为性能瓶颈。",
    "source": "文档中「5. 数据库唯一索引」的「优缺点」部分",
    "hint": "考虑数据库在执行插入操作时，为了维护唯一性需要做什么额外的检查。"
  },
  {
    "question": "在RabbitMQ中，与自动确认模式相比，手动确认模式如何提高了系统的可靠性？",
    "options": [
      "它允许在消费者成功处理完消息后才发送确认，若处理失败，消息可以被重新投递。",
      "它在消息被接收的瞬间就进行确认，从而加快了消息处理速度。",
      "它能保证每条消息绝对只被消费一次，彻底杜绝了重复消费的可能。",
      "它通过自动丢弃处理失败的消息来减轻消息队列的压力。"
    ],
    "correctAnswerIndex": 0,
    "explanation": "手动确认模式的核心优势在于，它将确认(ack)的主动权交给了消费者应用。只有当业务逻辑成功处理完消息后，消费者才显式发送确认。如果消费者在处理过程中崩溃或处理失败，消息就不会被确认，RabbitMQ会在之后将该消息重新投递给其他消费者，从而保证了消息不会丢失，提升了可靠性。",
    "source": "文档中「6. 消息确认机制」的「优缺点」部分",
    "hint": "思考一下，如果消费者拿到消息后、处理完成前就崩溃了，在哪种模式下消息不会丢失？"
  },
  {
    "question": "当在服务端（消费者端）实现消息去重时，文档提到哪种技术可以作为替代数据库或普通缓存的高性能去重方案？",
    "options": [
      "分布式锁 (Distributed Lock)",
      "布隆过滤器 (Bloom Filter)",
      "本地文件日志 (Local File Log)",
      "消息队列的优先级队列特性 (Priority Queue feature)"
    ],
    "correctAnswerIndex": 1,
    "explanation": "文档在“服务端去重”策略中明确提到“使用布隆过滤器：布隆过滤器是一种高效的概率型数据结构，可以快速判断某个元素是否存在于集合中，适用于高性能去重场景。”这使其成为替代传统数据库查询的一种高效选择。",
    "source": "文档中「2. 服务端去重」的「消息 ID 存储和去重」部分",
    "hint": "文档中描述了一种“高效的概率型数据结构”用于此场景。"
  },
  {
    "question": "根据“幂等性设计的挑战”一节，实现幂等性设计可能给系统带来什么问题？",
    "options": [
      "降低了系统的安全性和数据的保密性。",
      "削弱了系统的分区容错能力。",
      "增加了系统的复杂性和潜在的性能开销。",
      "导致业务逻辑难以进行单元测试。"
    ],
    "correctAnswerIndex": 2,
    "explanation": "文档中明确指出了实现幂等性设计的两大挑战：一是“系统复杂性”，即需要对每个操作进行详细的设计和实现；二是“性能开销”，例如存储和查询请求ID会带来额外的系统负担。因此，在享受幂等性带来的可靠性的同时，也必须考虑其引入的复杂度和性能成本。",
    "source": "文档中「幂等性设计去重」的「3. 幂等性设计的挑战」部分",
    "hint": "思考一下，为了让每个操作都变为幂等，开发者需要付出哪些额外的“代价”。"
  }
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                '<div class="score-card score-details">' +
                '<table>' +
                '<tr><td>正确</td><td>' + correctCount + '</td></tr>' +
                '<tr><td>错误</td><td>' + wrongCount + '</td></tr>' +
                '<tr><td>未答</td><td>' + skippedCount + '</td></tr>' +
                '</table>' +
                '</div>' +
                '</div>' +
                '<div class="report-actions">' +
                '<button id="review-btn">回顾测验</button>' +
                '<button class="primary" onclick="location.reload()">再试一次</button>' +
                '</div>' +
                '</div>';

            let reportEl = document.querySelector('.final-score-container');
            if (reportEl) {
                reportEl.remove();
            }
            quizContentEl.insertAdjacentHTML('beforeend', finalScoreHtml);
            document.getElementById('review-btn').addEventListener('click', reviewQuiz);
        }

        function reviewQuiz() {
            const reportEl = document.querySelector('.final-score-container');
            if (reportEl) {
                reportEl.remove();
            }

            document.querySelector('.quiz-body').style.display = 'block';
            document.getElementById('quiz-footer').style.display = 'flex';
            
            currentQuestionIndex = 0;
            loadQuestion();
            updateNavigationButtons(); // Ensure nav buttons are correctly displayed
        }

        // Event Delegation for options
        optionsListEl.addEventListener('click', (event) => {
            if (event.target && event.target.matches('li.option-item')) {
                // Check if the question has already been answered
                if (userAnswers[currentQuestionIndex] === null) {
                    const selectedIndex = parseInt(event.target.dataset.index, 10);
                    selectOption(selectedIndex);
                }
            }
        });

        prevBtn.addEventListener('click', goToPreviousQuestion);
        nextBtn.addEventListener('click', goToNextQuestion);
        hintBtn.addEventListener('click', showHint);
        
        // 初始加载
        loadQuestion();
      </script>
    </body>
    </html>
  